Notwithstanding recent advances in substation automation, no existing protocol integrates human–machine interaction, intelligent interlocking, operational autonomy, and artificial intelligence analysis in sequential maneuvering contexts. This study proposes an automated interface to optimize and control switching operations in electrical substations by integrating operational protocols, automated documentation generation, and artificial intelligence techniques with interactive graphical visualization. The developed solution enables sequential command execution, classification of operational events, and automatic generation of auditable reports, enhancing accuracy and traceability in operations. A total of 108 real files, corresponding to 54 events with documented failures, were analyzed and used to train and validate a recurrent convolutional neural network model. The system achieved an accuracy of 82.92% in error detection, along with reductions of 42.7% in the average operational response time and 38.5% in failure frequency. In addition to standardizing procedures, the interface demonstrated adaptability to different substation topologies and configurations, establishing itself as a scalable, secure, and efficient alternative for assisted operation environments. The results suggest that the proposed solution contributes to reducing inconsistencies, increasing decision-making autonomy, and strengthening operational safety in the power sector.

This work proposes the development of a metric for the analysis of operational robustness in systems, focusing on performance, complexity, and stability as key components. The methodology integrates these factors, enabling the assessment of the system’s ability to meet its design requirements, its internal dynamics and external interactions, and its capacity to return to equilibrium after disturbances. The metric is applied in three case studies: an intensive care unit , process scheduling in operating systems, and traction and braking in electric vehicles. The results show that, in scenarios with higher robustness, the contributions of performance, complexity and stability are balanced, with performance contributing around 30% and complexity and stability each contributing approximately 35%. In contrast, scenarios with lower robustness exhibit greater variation in the contributions of these components. These findings suggest that the proposed metric is an efficient tool for both quantitative and qualitative analyses, providing more detailed perspectives for decision making in complex systems.

Nowadays, handwriting still plays an important role in our lives, even though we live in a progressively more digital era. Therefore, losing the ability to write, whether because of physical or psychological complications, can become a huge handicap. The system described herein aims, in a first stage, to enable handwriting training through haptic guidance. The implementation was performed in Visual Studio and with the use of OpenHaptics toolkit. The second stage consists in character classification, written using a haptic joystick. The pre-eminent procedure for digit classification consists in the use of Histogram of Oriented Gradients coupled with a multiclass Support Vector Machine (HOG-SVM) whereas for letters the strategy involves using a Convolutional Neural Network (CNN).

Vertical Ground Reaction Forces (VGRF) reference profiles of human gait are important in medicine, for the recognition of gait disorders and in the assessment of rehabilitation treatments. A walking human is a dynamic varying system and in spite of the VGRF reference dependence on several patient's variables, doctors traditionally use the same static reference for all patients. The purpose of this study is to find out if an Extreme Learning Machine (ELM) is adequate to generate the dynamic VGRF reference profiles of healthy people depending on the subject's age, weight, height and stride duration. The ELM is compared with two other baseline Computational Intelligence (CI) methods, the Backpropagation Neural Network (BNN) and Multioutput Support Vector Regression (MSVR). Data from 28 healthy males walking at five different stride durations, collected using instrumented shoes, were used to train and test the CI models. The results showed that ELM is a well suited method to generate the dynamic VGRF reference profile for both dominant and non-dominant limbs, showing the lowest root mean square errors for the test set, 0.0201 and 0.0243 (fraction of body weight) for the dominant and non-dominant limbs respectively. This study reveals a promising methodology that can be implemented in real time gait analysis, allowing doctors to find a specific reference gait pattern for the gait analysis of an unhealthy person by specifying the age, weight, height and stride duration.

This paper introduces two new important issues to be considered in the design of the zero moment point (ZMP) trajectory reference for the sagittal plane balance control of an autonomous walking biped robot with an human-like gait.

The real-time balance control of an eight-link biped robot using a zero moment point (ZMP) dynamic model is difficult due to the processing time of the corresponding equations. To overcome this limitation, two alternative intelligent computing control techniques were compared: one based on support vector regression (SVR) and another based on a first-order Takagi-Sugeno-Kang (TSK)-type neural-fuzzy (NF) network. Both methods use the ZMP error and its variation as inputs and the output is the correction of the robot's torso necessary for its sagittal balance. The SVR and the NF were trained based on simulation data and their performance was verified with a real biped robot. Two performance indexes are proposed to evaluate and compare the online performance of the two control methods. The ZMP is calculated by reading four force sensors placed under each robot's foot. The gait implemented in this biped is similar to a human gait that was acquired and adapted to the robot's size. Some experiments are presented and the results show that the implemented gait combined either with the SVR controller or with the TSK NF network controller can be used to control this biped robot. The SVR and the NF controllers exhibit similar stability, but the SVR controller runs about 50 times faster.

Despite all the progress that has been made in Robotics and Artificial Intelligence, traditional approaches seem unsuitable to build truly intelligent robots, exhibiting human-like behaviours. Many authors agree that the source of intelligence is, to a great extent, the use of a huge memory, where sequences of events that guide our later behaviour are stored. Inspired by that idea, our approach is to navigate a robot using sequences of images stored in a Sparse Distributed Memory-a kind of associative memory based on the properties of high dimensional binary spaces, which, in theory, exhibits some human-like behaviours. The robot showed good ability to correctly follow most of the sequences learnt, with small errors and good immunity to the kidnapped robot problem.

This paper describes the control of an autonomous biped robot capable to be subjected to external pushing forces applied in the sagittal plane. A 1st order Takagi-Sugeno-Kang (TSK) type neuro fuzzy net, that was trained based on experimental and simulation data, is used. This net uses the zero moment point (ZMP) position and its variation as inputs, and the sagittal correction of the robotpsilas body is obtained as the output. The ZMP is obtained be acquiring use of the force distribution on four force sensors placed under each robotpsilas foot. The gait implemented on our biped is similar to a human gait. A human gait was acquired and adapted to the robot size. Some experiments were carried out and the results show that the used gait combined with the TSK neuro fuzzy net is appropriate to be used in the control of a biped robot, with and without an applied external force.